Quantitative Results of Edges and Lines
We show quantitative results of edges and lines on Indoor and Places2 in Tab. REF . Our TSR can get much better results on both Indoor and Places2 compared with MST. It demonstrates that the transformer-based TSR is amenable to learning holistic structures in a sparse tensor space, which can benefit the results of ZITS a lot as shown in Tab. REF . Note that TSR results in Tab. REF  are based on Mask-Predict [1]}, which can enrich the structural generation by iteratively sampling outputs but does not improve the quantitative metrics a lot. More about Mask-Predict are discussed in the supplementary.
